Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users' provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users' perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.
翻译:有效探索大量数据以作出决定,类似于回答一个复杂问题,这在很多现实世界应用情景中具有挑战性。在这方面,自动汇总具有重大重要性,因为它将为大数据分析奠定基础。传统的汇总方法优化了系统,以生成一个简短的静态摘要,以适应所有不考虑汇总主观性的用户,即对于不同用户而言,什么是有价值的,使这些方法在现实世界使用案例中不切实际。本文件提议了一个交互式基于概念的汇总模式,称为适应摘要,帮助用户编写他们想要的汇总,而不是制作一个单一的不灵活摘要。该系统从用户那里逐渐获得信息,同时通过互动回路与系统互动。用户可以选择拒绝或接受选择选择在摘要中包含的概念的行动,而这一概念从用户的角度和他们的反馈的信心水平中具有重要性。拟议的方法可以保证互动速度,使用户参与这一过程。此外,它消除了参考摘要的必要性,因为参考摘要是总结中具有挑战性的问题,而用户在进行互动回馈时,通过互动回路进行互动回馈。用户们可以选择拒绝或接受在摘要中选择选择选择一个具有重要性的概念,从用户视角中选择。